Matej commited on
Commit
0e4d3e5
1 Parent(s): 14d29bb

update my_app.py

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Files changed (1) hide show
  1. my_app.py +7 -14
my_app.py CHANGED
@@ -4,17 +4,12 @@ from huggingface_hub import from_pretrained_keras
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  from tensorflow.keras import mixed_precision
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  # Load your trained models
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- #model1 = from_pretrained_keras("ml-debi/EfficientNetB0-Food101")
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  #model2 = from_pretrained_keras("ml-debi/EfficientNetB0-Food101")
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- # Try different keras model
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- model1 = from_pretrained_keras("NikiTricky/resnet50-food101")
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-
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  with open('classes.txt', 'r') as f:
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  classes = [line.strip() for line in f]
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- print("Class names: ", classes)
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-
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  # Add information about the models
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  model1_info = """
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  ### Model 1 Information
@@ -31,9 +26,8 @@ This model is based on the EfficientNetB0 architecture and was trained on the Fo
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  def preprocess(image):
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  print("before resize", image.shape)
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  image = tf.image.resize(image, [224, 224])
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-
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  image = tf.expand_dims(image, axis=0)
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-
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  print("After expanddims", image.shape)
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  return image
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@@ -48,17 +42,16 @@ def predict(image):
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  image = preprocess(image)
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  print(mixed_precision.global_policy())
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- prediction = model1.predict(image)
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  print("model prediction", prediction)
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- predicted_class = classes[int(tf.argmax(prediction, axis=1))]
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- confidence = tf.reduce_max(prediction).numpy()
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- return predicted_class, confidence
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  iface = gr.Interface(
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  fn=predict,
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  inputs=[gr.Image()],
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- outputs=[gr.Textbox(label="Predicted Class"), gr.Textbox(label="Confidence")],
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- title="Transfer Learning Mini Project",
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  description=f"{model1_info}\n",
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  )
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  from tensorflow.keras import mixed_precision
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  # Load your trained models
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+ model1 = from_pretrained_keras("ml-debi/EfficientNetB0-Food101")
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  #model2 = from_pretrained_keras("ml-debi/EfficientNetB0-Food101")
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  with open('classes.txt', 'r') as f:
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  classes = [line.strip() for line in f]
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  # Add information about the models
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  model1_info = """
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  ### Model 1 Information
 
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  def preprocess(image):
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  print("before resize", image.shape)
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  image = tf.image.resize(image, [224, 224])
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+
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  image = tf.expand_dims(image, axis=0)
 
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  print("After expanddims", image.shape)
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  return image
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  image = preprocess(image)
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  print(mixed_precision.global_policy())
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+ prediction = model1.predict(image)[0]
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  print("model prediction", prediction)
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+ confidences = {model1.config['id2label'][str(i)]: float(prediction[i]) for i in range(101)}
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+ return confidences
 
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  iface = gr.Interface(
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  fn=predict,
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  inputs=[gr.Image()],
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+ outputs=[gr.Label(num_top_classes=5)],
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+ title="Food Vision Mini Project",
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  description=f"{model1_info}\n",
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  )
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